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As a way to understand recent, general hedge fund positioning, we use the monthly returns to determine the 2020 factor exposures of major hedge fund categories using HFR indexes. 


First, a caveat: while we are comfortable with only 12 data points for monthly returns-based analysis (read this blog post where we tested out-of-sample r-squareds using 12 monthly data points), we realize that 2020 was an exceptional year with relatively extreme market moves. This analysis displays the average factor exposures of hedge funds over 2020, even though it is very likely that active hedge funds rotated a fair amount during the year. 


To validate that this exercise provides value to the reader, we took the indexes’ factor exposures using returns data over the 12 months ending November 2020 and estimated each index’s December 2020 performance. We compared these factor-based estimates to the indexes’ actual returns in December 2020.1 As seen in the Appendix, Venn directionally estimated all hedge fund categories correctly, yet consistently underestimated their returns (by about half in most cases, e.g., we expected the HFRI Fund Weighted Index to return 2.13% vs. its actual performance of 4.56%). So, as with all statistical exercises, there is some error, but also hopefully some value to be gleaned in the analysis that follows.


Hedge Fund Factor Exposures in 2020

  • We find that every hedge fund index except one exhibited positive Equity market exposure in 2020. Equity Hedge led the pack with a 0.72 Equity beta. Macro was the category that appeared to be the most neutrally positioned relative to global equity markets.
  • Macro exhibited a positive exposure to the Foreign Currency factor, which indicates that macro funds on average were positioned short the USD and long other G10 currencies, perhaps due in part to rising inflation concerns in the U.S.
  • There were a few interesting style factor exposures:
    • The Macro category showed significant exposures to two macro style factors.
      • A negative exposure to the Foreign Exchange Carry factor implies that Macro hedge funds on average might have invested in lower-yielding currencies and shorted the opposite. This exposure reinforces what we observed for the Foreign Currency factor, as the USD was considered a relatively high-yielding carry currency for the majority of 2020.
      • A positive exposure to the Trend Following factor was driven by Macro funds likely intentionally following trends specifically in currency, equity, and commodities markets, as determined from further analysis on Venn.
    • The Event-Driven category’s negative Quality exposure might mean Event-Driven funds on average had exposure to junkier stocks in their long portfolios and high-quality stocks in their short portfolios. To the extent these Event-Driven funds are focusing on merger arbitrage strategies, this could imply that junky companies were acquired by relatively higher-quality companies.

2020 Factor Exposures for HFR Indices2

Time period: January 2020 - December 2020. Source: Venn as of February 18, 2021, using monthly data.


Visit Venn to:

  • Analyze factor exposures for specific funds 
  • Use factor exposures to inform forward-looking return expectations for funds
  • Use factor exposures to receive return estimates of recent fund performance, namely for funds that don’t report their returns on a daily basis

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Appendix: Expected vs. Actual December 2020 Returns for the HFR Indices

Time period: December 2019 - November 2020 to determine factor exposures for the “Expected” returns. December 2020 for the “Actual” returns. Source: Venn as of February 18, 2021, using monthly data.


 

REFERENCES

1The “actual returns” for December 2020 in our analysis are estimates as of February 18, 2021.

2The factor exposures were determined using Venn’s factor selection methodology. Blank factor exposures indicate that the factor was removed by a Lasso regression, which is used for factor selection. The factors that were not removed by the Lasso regression were then used in an Ordinary Least Squares regression to determine the betas displayed in this table.

 

References to the Two Sigma Factor Lens and other Venn methodologies are qualified in their entirety by the applicable documentation on Venn.

This article is not an endorsement by Two Sigma Investor Solutions, LP or any of its affiliates (collectively, “Two Sigma”) of the topics discussed. The views expressed above reflect those of the authors and are not necessarily the views of Two Sigma. This article (i) is only for informational and educational purposes, (ii) is not intended to provide, and should not be relied upon, for investment, accounting, legal or tax advice, and (iii) is not a recommendation as to any portfolio, allocation, strategy or investment. This article is not an offer to sell or the solicitation of an offer to buy any securities or other instruments. This article is current as of the date of issuance (or any earlier date as referenced herein) and is subject to change without notice. The analytics or other services available on Venn change frequently and the content of this article should be expected to become outdated and less accurate over time. Any statements regarding planned or future development efforts for our existing or new products or services are not intended to be a promise or guarantee of future availability of products, services, or features.  Such statements merely reflect our current plans.  They are not intended to indicate when or how particular features will be offered or at what price.  These planned or future development efforts may change without notice. Two Sigma has no obligation to update the article nor does Two Sigma make any express or implied warranties or representations as to its completeness or accuracy. This material uses some trademarks owned by entities other than Two Sigma purely for identification and comment as fair nominative use. That use does not imply any association with or endorsement of the other company by Two Sigma, or vice versa. See the end of the document for other important disclaimers and disclosures. Click here for other important disclaimers and disclosures.

This article may include discussion of investing in virtual currencies. You should be aware that virtual currencies can have unique characteristics from other securities, securities transactions and financial transactions. Virtual currencies prices may be volatile, they may be difficult to price and their liquidity may be dispersed. Virtual currencies may be subject to certain cybersecurity and technology risks. Various intermediaries in the virtual currency markets may be unregulated, and the general regulatory landscape for virtual currencies is uncertain. The identity of virtual currency market participants may be opaque, which may increase the risk of market manipulation and fraud. Fees involved in trading virtual currencies may vary.

 

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